Add histogram plotting code
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							| @@ -1,6 +1,6 @@ | |||||||
| # Neural Architecture Search Without Training | # [Neural Architecture Search Without Training](https://arxiv.org/abs/2006.04647) | ||||||
|  |  | ||||||
| This repository contains code for replicating our paper on NAS without training. | This repository contains code for replicating our paper, [NAS Without Training](https://arxiv.org/abs/2006.04647). | ||||||
|  |  | ||||||
| ## Setup | ## Setup | ||||||
|  |  | ||||||
| @@ -35,7 +35,7 @@ Each command will finish by calling `process_results.py`, which will print a tab | |||||||
| | Ours (N=100) |          17.4139  | 88.45 +- 1.46    | 91.61 +- 1.71     | 66.42 +- 3.27     | 66.56 +- 3.28      | 36.56 +- 6.70          | 36.37 +- 6.97 | | Ours (N=100) |          17.4139  | 88.45 +- 1.46    | 91.61 +- 1.71     | 66.42 +- 3.27     | 66.56 +- 3.28      | 36.56 +- 6.70          | 36.37 +- 6.97 | ||||||
|  |  | ||||||
|  |  | ||||||
| To try different sample sizes, simply change the `--n_samples` argument in the call to `search.py`, and update the list of sample sizes on line 51 of `process_results.py`. | To try different sample sizes, simply change the `--n_samples` argument in the call to `search.py`, and update the list of sample sizes [this line](https://github.com/BayesWatch/nas-without-training/blob/master/process_results.py#L51) of `process_results.py`. | ||||||
|  |  | ||||||
| Note that search times may vary from the reported result owing to hardware setup. | Note that search times may vary from the reported result owing to hardware setup. | ||||||
|  |  | ||||||
| @@ -56,3 +56,18 @@ The code is licensed under the MIT licence. | |||||||
| ## Acknowledgements | ## Acknowledgements | ||||||
|  |  | ||||||
| This repository makes liberal use of code from the [AutoDL](https://github.com/D-X-Y/AutoDL-Projects) library. We also rely on [NAS-Bench-201](https://github.com/D-X-Y/NAS-Bench-201). | This repository makes liberal use of code from the [AutoDL](https://github.com/D-X-Y/AutoDL-Projects) library. We also rely on [NAS-Bench-201](https://github.com/D-X-Y/NAS-Bench-201). | ||||||
|  |  | ||||||
|  | ## Citing us | ||||||
|  |  | ||||||
|  | If you use or build on our work, please consider citing us: | ||||||
|  |  | ||||||
|  | ``` | ||||||
|  | @misc{mellor2020neural, | ||||||
|  |     title={Neural Architecture Search without Training}, | ||||||
|  |     author={Joseph Mellor and Jack Turner and Amos Storkey and Elliot J. Crowley}, | ||||||
|  |     year={2020}, | ||||||
|  |     eprint={2006.04647}, | ||||||
|  |     archivePrefix={arXiv}, | ||||||
|  |     primaryClass={cs.LG} | ||||||
|  | } | ||||||
|  | ``` | ||||||
|   | |||||||
| @@ -60,7 +60,7 @@ def decide_plot(acc, plt_cts, num_rows, boundaries=[60., 70., 80., 90.]): | |||||||
| if __name__ == '__main__': | if __name__ == '__main__': | ||||||
|     parser = argparse.ArgumentParser(description='Plot histograms of correlation matrix') |     parser = argparse.ArgumentParser(description='Plot histograms of correlation matrix') | ||||||
|     parser.add_argument('--data_loc', default='../datasets/cifar/', type=str, help='dataset folder') |     parser.add_argument('--data_loc', default='../datasets/cifar/', type=str, help='dataset folder') | ||||||
|     parser.add_argument('--api_loc', default='../datasets/NAS-Bench-201-v1_1-096897.pth', |     parser.add_argument('--api_loc', default='NAS-Bench-201-v1_1-096897.pth', | ||||||
|                     type=str, help='path to API') |                     type=str, help='path to API') | ||||||
|     parser.add_argument('--arch_start', default=0, type=int) |     parser.add_argument('--arch_start', default=0, type=int) | ||||||
|     parser.add_argument('--arch_end', default=15625, type=int) |     parser.add_argument('--arch_end', default=15625, type=int) | ||||||
|   | |||||||
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